An efficient deep-learning tool for detecting eye disease

Model scans images to detect urgent signs of conditions leading to blindness.

A new artificial-intelligence tool deploys a highly efficient form of deep learning to diagnose eye disease from medical images.

Convolutional neural networks are deep-learning algorithms adept at processing images, but researchers typically need to train them on more than a million medical images before they can test how well the algorithms work. Kang Zhang at the University of California, San Diego, in La Jolla and his colleagues created a kind of convolutional neural network capable of learning with many fewer images.

The team trained the model on 108,000 images of retinas. All had been classified by experts as either healthy or showing signs of a leading cause of blindness: macular degeneration or diabetic macular edema, a build-up of fluid in the retina. The algorithm identified critical cases of these conditions as accurately as six experts in ophthalmology.

The model also identified pediatric pneumonia from chest X-rays, suggesting that the technique could be broadly applied across medicine.